Identification of Potential Mediators Between Depression and Fluid Adherence in Older Adults Undergoing Hemodialysis Treatment.
Fluid management can be complicated by depression. Depression is common in adults undergoing HD, with approximately 30% of this population experiencing depression (Cukor, Rosenthal, Jindal, Brown, & Kimmel, 2009; Khalil, Frazier, Lennie, &
Sawaya, 2011). The potential consequences of depression in adults with ESRD are poor quality of life, frequent hospitalizations, and increased mortality (Feng, Yap, & Ng, 2013; Finkelstein, Wuerth, Troidle, & Finkelstein, 2008; Kalendar, Ozdemir, & Koroglu, 2005). These consequences are especially concerning in older adults, the fastest growing segment of the ESRD population, because depression is their most commonly experienced mental health condition that often goes undetected, and it is exacerbated when combined with a physical illness (Lebowitz et al., 1997). While psychosocial interventions to treat depression in older adults with ESRD have shown promise (Erdley et al., 2014), factors that mediate negative effects of depression in the ESRD population remain underidentified (Kimmel, 2000). Few studies have examined the relationship of depression and other modifiable factors to fluid adherence solely among older adults undergoing HD. Therefore, there is a need to understand these factors to help inform practice initiatives aimed at improving fluid adherence. The purpose of this study was to identify potential mediators between depression and fluid adherence.
Factors Associated with Fluid Management
There is some evidence that self-efficacy, social support, and demographic factors are associated with fluid intake. Self-efficacy is a belief in one's ability to successfully manage HD-related tasks. There is a positive relationship between self-efficacy and fluid adherence (Wells & Anderson, 2011). High self-efficacy is associated with decreased hospitalizations and improved quality of life in individuals undergoing HD (McMurray, Johnson, Davis, & McDougall, 2002). Further, the presence of a social support system, such as family, friends, and medical providers, helps individuals undergoing HD successfully manage fluid intake (Browne, 2011; Smith et al., 2010). Considering these variables are associated with fluid management, they are included in the analysis of this study. We hypothesized that self-efficacy and social support are mechanisms that mitigate the negative relationship between depression and fluid intake.
Subjects and Recruitment
Participants were recruited from four HD facilities in the southeastern United States. Facility social workers distributed recruitment flyers to patients meeting eligibility criteria (aged 50 years and older, undergoing in-center HD for at least 30 days, community-dwelling, and English-speaking). Patients who were cognitively or physically unable to complete an interview based on staff assessment were excluded from recruitment. Next, facility staff provided names and treatment schedules of eligible patients to the interviewers. Eligible patients were approached during their HD treatment, provided a description of the purpose of the study, and invited to participate. Informed consent was obtained prior to the interview. Patients received a $10 store gift card for participating in the study. The study was approved by the Institutional Review Board of the University of North Carolina at Chapel Hill.
Interviews occurred during each participant's HD treatment by a master's level social worker or a registered nurse. Most participants agreed to be interviewed immediately upon learning about the study and providing consent; others requested the interviewer to return during their next treatment. In the event a participant could not complete the interview during his or her treatment, the remainder of the interview was conducted during a future treatment. Response cards were provided for each set of Likert-scale questions, and participants selected their responses accordingly.
Basic demographic information, including age, race, dialysis vintage, education, marital status, and living situation (alone, with others, or in a group setting), was obtained to establish a demographic profile of the participants. Information was obtained regarding health, disability, and cognition. Health status was obtained using the Self-Rated Health 5-item questionnaire, a subscale of the Short Form-36 ([alpha] = 0.92) (Ware & Sherbourne, 1992). Respondents were asked to rate their health as excellent, very good, good, fair, or poor (dichotomized good or better vs. fair or poor in analyses). Disability status was measured using the 8-item Stanford Health Assessment Questionnaire (HAQ) ([alpha] = 0.85) (Lorig Sobel, Ritter, Laurent, & Hobbs, 2001). Responses range from "0 = without any difficulty" to "3 = unable to do" (related to getting out of bed and getting in and out of a car). Cognitive status was evaluated using a modified version of the Saint Louis University Mental Status Examination (SLUMS) (Banks & Morely, 2003). Due to dexterity limitations of some patients during treatment (i.e., being connected to the machine) and in consultation with the developers of the SLUMS (Nina Tumosa, personal communication, July 13, 2012), two items were removed from the 11-item questionnaire that would require use of a writing utensil. Thus, cognitive status was determined based on percentage of overall scores corresponding with percentages of the original version.
Depression. The Geriatric Depression Scale (GDS) 15-Item Short Form ([alpha] = 0.86) (Sheikh & Yesavage, 1986) was used as an indicator of potential depression. The GDS effectively detects depressive symptoms in older adults with ESRD (Balogun, Turgut, Balogun, Holroyd, & AbdelRahman, 2011) and other chronic illness (Greenberg, 2007). This dichotomous instrument asks questions like: "Are you basically satisfied with life (yes or no) ?" A score of 5 or higher is suggestive of depression.
Social support. Social support was measured using the 18-item Lubben Social Network Scale (LSNS18). The LSNS-18 captures three sources of social support of older adults (family, friends, and neighbors) related to emotional supports, perceived tangible supports, and network size ([alpha] = 0.82) (Lubben & Gironda, 2003). Example instrument items are: "How many of your relatives do you see or hear from at least once a month?" and "How many friends do you feel at ease with that you can talk about private matters?"
Self-efficacy. The Diabetes Self-Efficacy Scale's items were modified to be more consistent with individuals undergoing HD ([alpha] = 0.85) (Lorig et al., 2001). The scale was originally designed in Spanish and used in a study with 20 participants; the English version is now being used in the Stanford English Diabetes Self-Management study. Ten items (i.e., meals/nutrition, exercise, communication with physicians, symptom management, and medication management) were used to construct a self-efficacy scale that related to daily self-management tasks for adults with ESRD. An expert panel of three social workers, three dietitians, and a physician with at least two years' work experience in HD settings were recruited to discuss the proposed items and make recommendations for revising the items accordingly (DeVellis, 2003). The number of items was reduced to eight following expert panel feedback. Four cognitive interviews were conducted with older adults undergoing HD to ensure that the new questions were written from the respondent's perspective to achieve construct validity (Beatty & Willis, 2007). The final scale resulted in eight questions, and participants rated their level of confidence about performing ESRD-related tasks from 0 (not at all confident) to 10 (totally confident). An example instrument item is: "How confident do you feel that you can prepare kidney friendly meals?"
Fluid management. Fluid management was measured using the fluid frequency subscale of the Dialysis Diet and Fluid Adherence Questionnaire (DDFQ). The DDFQ was designed to evaluate adherent behavior of adults undergoing HD ([alpha] = 0.81) (Kara, Caglar, & Kilic, 2007; Vlaminck, Maes, Jacobs, Reytjens, & Evers, 2001). The frequency of fluid nonadherence was obtained by asking participants: "How many days during the past 14 days didn't you follow your fluid guidelines?" To compare adults undergoing HD who were completely adherent to those who were not, and to establish a binary outcome variable required for logistic regression, fluid adherence was dichotomized using a score of 0 (1 or more days nonadherent) and 1 (completely adherent; no days nonadherent).
Descriptive statistics. All data were checked for missing values and outliers. Descriptive statistics by the level of depression, including means, standard deviations, and percentages, were calculated to describe the sample. Depression was dichotomized using 5 or greater as the cutoff score (i.e., per the scoring criteria for the GDS, with scores 5 or greater indicating depressed). Chi-square and two-tailed t test were used to compare the means between groups.
Correlations. The association of fluid adherence and age group was analyzed using the Kruskall-Wallis test. The correlation coefficients were obtained and interpreted using a significance level of p <0.05 and p <0.01.
Logistic regression models. Multivariate logistic regression was employed to examine the relationship between depression and fluid adherence to yield adjusted odds ratios (AORs) and associated 95% confidence intervals (CIs). Four models were analyzed: 1) the primary independent variable (depression); 2) the primary independent variable (depression) with a secondary independent variable (social support); 3) the primary independent variable (depression) with a secondary independent variable (self-efficacy); and 4) the primary independent variable (depression) with both secondary independent variables (self-efficacy and social support). Goodness-of-fit was assessed using the Hosmer-Lemeshow goodness-of-fit test (with p>0.05 indicating good fit). Each model's pseudo r-square was calculated to determine the percentage of variance explained by the independent variables. A likelihood ratio test was performed to test the joint significance of the independent variables in the models. Health status, disability status, and demographic variables of age, months on hemodialysis, sex, race, marital status, education, and living situation were controlled in each model.
The Breusch-Pagan test was used to detect heteroskedasticity in which p > 0.05 indicates heteroskedasticity. The degree of multicollinearity was checked using the mean variance inflation factor (VIF) for all independent variables, with VIF greater than 10 indicating multicollinearity (UCLA Statistical Consulting Group).
Descriptive analyses, correlation analyses, logistic regression, and regression diagnostics were performed using Stata 12.0 (StataCorp, 2012). Missing data were treated using casewise deletion. Less than 5% of observations with missing values were excluded from the analysis (n = 5).
Participant characteristics are presented in Table 1. In total, 123 patients were approached for interviews. Among them, four terminated due to cognitive or physical difficulties (i.e., a participant was feeling unwell), and 12 refused to participate, resulting in an 87% participation rate (n = 107).
The mean age of participants was 63 years (SD = 8.6), and the average dialysis vintage was 86 months. The sample was divided nearly evenly between males (51%) and females (49%). Thirty percent graduated high school or obtained a GED, and 40% were married. Most participants (65%) were Black, reported a poor or fair health status (54%), and lived with others in a private residence (64%).
Most participants in the sample were not depressed based on the GDS results (GDS less than 5, n = 86, 80%). Statistically significant group differences were found in marital status (p = 0.004), health status (p = 0.002), disability status (p = 0.0035), and self-efficacy (p = 0006). Specifically, all single participants were not depressed, whereas 17% of married, 20% of divorced, 24% of widowed, and 57% of participants with other marital statuses (i.e., not married but partnered or separated) were depressed. Proportionately, more people in poor or fair health were depressed (33%) compared to those in better health (9%). There were higher mean scores of disability in the depressed group than the not depressed group (0.36 and 0.16, respectively). There were
Column percentages presented in table. slightly higher self-efficacy mean scores in the not depressed group (n = 69) than in the depressed group (n = 61).
Correlations are presented in Table 2. There was a statistically significant relationship between age group and fluid adherence ([chi square] = 11.65, p <0.05). The rate of adherence increased with age. Overall, the sample had slightly more adherent participants than non-adherent (53% compared to 47%). The age group of participants 50 to 59 years had the highest rate of non-adherence.
Table 3 presents the AORs and CIs of fluid adherence and its association with depression, social support, self-efficacy, and demographic variables. In model one, being depressed decreased the odds of fluid adherence by 18% (AOR = 0.82, 95% CI = 0.67, 0.99). For every year increase in age, the odds of fluid adherence increased by 8% (AOR = 1.08, 95% CI = 1.02, 1.14). The pseudo r-square of this model was 0.15 (15% of the variance in fluid adherence was explained by the independent variables). Model two added social support and showed the same results (being depressed decreased the odds of fluid adherence by and 18% every year increase in age increased the odds of fluid adherence by 8%; pseudo r-squared = 0.15). Model three added self-efficacy, removed social support, and eliminated the association of depression with fluid adherence, and itself related to more adherence. As self-efficacy scores increased by one point (scores ranged from 39 to 80; M = 67.5, SD = 8.96), the odds of fluid adherence increased by 9% (pseudo r-square 0.21). In model four, all combined independent variables similarly explained 21% of the variance in self-management, with results virtually identical to model three.
The Hosmer-Lemeshow goodness-of-fit test indicated good fit in all models (p = 0.19 to 0.27). The likelihood ratio test was statistically significant ([chi square] = 9.01, p<0.05) indicating the variables jointly contribute to fluid adherence (note: models one, two, and three are nested in model four). Heteroskedasticity was not detected in the models using the Breusch-Pagan test (p = 0.90 to 0.99). The VIF test did not indicate the existence of multicollinearity (mean VIF = 1.44 to 1.46).
The objective of this study was to identify factors that can potentially mediate the negative impact of depression on successful self-management of fluid intake. Findings of this study can help support nephrology nurses, social workers, and other interdisciplinary team members gain insight into factors associated with fluid adherence. Identifying mediating factors associated with fluid adherence can inform the development of psychosocial interventions for persons with ESRD, especially older patients who may benefit from the enhanced ability to engage in self-management behaviors (Washington, Zimmerman, & Browne, 2016).
In this study, 20% of the sample were depressed, and less than half successfully managed their fluid intake. Depending upon how it is measured (i.e., self-report, interdialytic weight gain), up to 70% of patients are nonadherent with fluid intake recommendations (Kugler, Vlaminck, Haverich, & Maes, 2005), largely due to psychosocial barriers, such as depression. While increased age has been associated with poor adherence in other disease states (Marcum et al., 2013), this study found that increased age was associated with better fluid adherence. This discovery is consistent with previous studies demonstrating that younger patients adhere less well to fluid management than older patients (Chilcot, Wellsted, & Farrington, 2009). Older adults tend to be more conservative when it comes to fluid intake as compared to younger adults (Hain, 2008; Victoria, Evangelos, & Zyga, 2015).
Another reason is related to how adherence is measured. In a review of 596 studies spanning 50 years of research on patient adherence to medical treatment, DiMatteo (2004) found that when collected by clinical measures, adherence was negatively associated with age, but when measured by self-report, adherence was positively associated with age. Third, it is possible that the older age group consumed less fluid because thirst sensation diminishes with age (Stachenfeld, DiPietro, Nadel, & Mack, 1997). These potential reasons are worth exploring further in this population.
Other demographic and health factors (months on dialysis, sex, race, marital status, health status, disability status, education, and living situation) were not found to have a statistically significant relationship with fluid adherence in this sample. It is possible that characteristics not accounted for in the logistic regression models, such as cognitive function, are important because evidence supports a relationship between cognitive function and adherence to the treatment regimen in older adults undergoing HD (Ham, 2008; Kurella Tamura & Yaffe, 2011). Cognitive function was not included in the logistic regression analysis because distributions were too small to examine a relationship (the small number of cases caused inflated standard errors and CIs, making results uninterpretable). However, a correlation between cognitive impairment and fluid adherence not presented in this article revealed cognition to be statistically correlated with fluid adherence (r = 0.27, p <0.001), such that as cognitive status scores increased (higher scores indicate more severe cognitive deficits), fluid adherence increased.
Previous studies have demonstrated the association between self-efficacy and fluid adherence, but few studies have examined the mediation potential of self-efficacy. The objective of this study was to identify individual-level factors that possibly mitigate negative effects of depression to fluid adherence to inform future intervention research. Findings of this study suggest the potential of intact self-efficacy to mitigate negative effects of depression. Depression was negatively associated with fluid adherence, but after adding self-efficacy to the model, the negative relationship between depression and fluid adherence disappeared, and the pseudo r-square increased, suggesting self-efficacy mediates depression and fluid adherence. Even in the presence of social support (which is an important predictor of fluid adherence), self-efficacy continued to weaken the association between depression and fluid adherence. Consequently, self-efficacy-boosting psychosocial interventions to reduce the high prevalence of depression in older adults undergoing HD may show promise. For example, although the sample size was small (n = 25 intervention group and n = 25 control group), Tsay and Hung (2004) found an empowerment program to increase self-efficacy and reduce depression in adults undergoing HD experiencing fluid adherence difficulties. Future studies should consider self-efficacy as a mediator between depression and fluid adherence.
Additional studies are needed to determine the exact mediation potential of self-efficacy. To date, most studies examining the relationship between adherence and depression in individuals undergoing HD have been correlational (Cukor et al., 2009). Statistical methods, such as structural equation modeling, can specify the causal direction implied by this study's findings (a direct path from depression to fluid adherence, and an indirect path mediated by selfefficacy). Given the sample size and the cross-sectional nature of these data, this study appropriately employed logistic regression, a useful analysis when examining the relationship between two or more independent variables to a dichotomous dependent variable (Kleinbaum & Klein, 2010). In addition, use of logistic regression to identify potential mediators has been used in previous research involving individuals living with chronic illness(es) (Bainbridge, Cheng, & Cowie, 2010).
The focus on the relationship between depression and adherence solely in older adults undergoing HD is a strength of this study. Given the high rates of depression in older HD patients (Palmer et al., 2013) and the growing prevalence of older adults undergoing HD (United States Renal Data System [USRDS], 2016), this study fills an important knowledge gap. This study used the GDS to measure depression. In a recent systematic review of studies examining the association of depression to psychosocial factors, only one of 57 studies used the GDS with participants undergoing HD despite the mean age of all participants was 53 (SD = 5.6) (Chan et al., 2011). Finally, the GdS effectively captures depression in older adults experiencing cognitive deficits and chronic disease.
The measurement of fluid intake used in this study is a limitation given its self-reported nature. Ideally, self-reported measures should be combined with clinical measures to increase reliability and validity. A more accurate measure of fluid intake is interdialytic weight gain (i.e., weight gained due to fluid intake between treatments) or dry weight (target or ideal weight) (Chilcot et al., 2010); although, even these clinical measures are problematic because a threshold of fluid intake to prevent complications has yet to be established (Cukor et al., 2009). Self-report and the lack of clinical thresholds may result in overeporting; for example, 50% of patients in one study self-reported nonadherence to fluid restrictions, but only 9% were actually nonadherent using interdialytic weight gain (Khalil et al., 2011). For these reasons, fluid intake was treated as completely adherent (no days nonadherent) vs. nonadherent to any extent (one or more days). Treating the variable as dichotomous divided the sample nearly even (55 vs. 49, respectively). However, to be certain findings were not a result of treating the variable as dichotomous, a linear regression containing all variables in model four was performed with fluid adherence as a continuous dependent variable, and provided similar results (self-efficacy and age were associated with fluid adherence at p = 0.047 and p = 0.034, respectively).
Further, fluid intake is only one aspect of the HD treatment regimen. Other aspects, such as diet and medication management (Browne & Merighi, 2010), are equally important. The relationship of these additional management behaviors to depression, self-efficacy, and social support are worth exploring. It is important to note these data were collected during summer months in the southeastern region of the United States. If thirst is a primary barrier to fluid adherence, it is reasonable to assume that patients had more difficulty staying within their recommended intake given the likelihood of increased thirst in summer months. Finally, because these data are cross-sectional, predictors of fluid intake cannot be examined.
On a regular basis, nephrology nurses, social workers, and other interdisciplinary team members are faced with the challenge of helping patients undergoing HD manage fluid intake through counseling and education (Kara, 2016). Findings from this study support the need to consider depression as a mediating factor worth evaluating in older adults who have problems maintaining the recommended fluid intake. Depression is a common mood disorder among adults undergoing HD, and is often under-recognized and undertreated in the older adult population, making it even more important to recognize and treat.
Author's Note: This work was supported by the John A. Hartford Geriatric Social Work Initiative and the University of North Carolina at Chapel Hill Institute on Aging Gordon H. DeFriese Research Award.
Statement of Disclosure: The authors reported no actual or potential conflict of interest in relation to this continuing nursing education activity.
Note: The Learning Outcome, additional statements of disclosure, and instructions for CNE evaluation can be found on page 259.
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Tiffany R. Washington, Debra J. Hain, Sheryl Zimmerman, Iris Carlton-LaNey
Tiffany R Washington, PhD, MSW, Assistant Professor, School of Social Work, University of Georgia, Athens, GA.
Debra J. Hain, PhD, is an Associate Professor, Christine E. Lynn College of Nursing, Florida Atlantic University, and a Nurse Practitioner, Cleveland Clinic Florida, Department of Nephrology, Boca Raton, FL. She is the ANNA Research Committee Chair and President-Elect of ANNA's Flamingo Chapter.
Sheryl Zimmerman, PhD, is the Kenan Distinguished Professor, School of Social Work, University of North Carolina at Chapel Hill, Chapel Hill, NC
Iris Carlton-LaNey, PhD, is the Berg-Beach Distinguished Professor, School of Social Work, University of North Carolina at Chapel Hill, Chapel Hill, NC.
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After completing this learning activity, the learner will be able to discuss the association between depression and fluid adherence for patients undergoing hemodialysis.
Learning Engagement Activity
For more information on depression in patients with chronic kidney disease, refer to the article entitled, "Examining Depression in Patients on Dialysis," by Allison A. Treadwell in the ANNA Online Library: https://library.annanurse.org/anna/ a rticles/1660/view
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Nephrology Nursing Journal Editorial Board
Statements of Disclosure
In accordance with ANCC governing rules Nephrology Nursing Journal Editorial Board statements of disclosure are published with each CNE offering. The statements of disclosure for this offering are published below.
Paula Dutka, MSN, RN, CNN, disclosed that she is a coordinator of Clinical Trials for the following sponsors: Amgen, Rockwell Medical, Keryx Biopharmaceuticals, Akebia Therapeutics, and Dynavax Technologies.
Norma J. Gomez, MBA, MSN, CNNe, disclosed that she is a member of the ZS Pharma Advisory Council.
Tamara M. Kear, PhD, RN, CNS, CNN, disclosed that she is a member of the ANNA Board of Directors, serves on the Scientific Advisory Board for Kibow Biotech, Inc., and is employed by Fresenius Kidney Care as an acute hemodialysis RN.
All other members of the Editorial Board had no actual or potential conflict of interest in relation to this continuing nursing education activity.
This article was reviewed and formatted for contact hour credit by Beth Ulrich, EdD, RN, FACHE, FAAN, Nephrology Nursing Journal Editor, and Sally Russell, mN, CMSRN, CPP, ANNA Education Director.
American Nephrology Nurses Association - Provider is accredited with distinction as a provider of continuing nursing education by the American Nurses Credentialing Center's Commission on Accreditation.
ANNA is a provider approved by the California Board of Registered Nursing, provider number CEP 00910.
This CNE article meets the Nephrology Nursing Certification Commission's (NNCC's) continuing nursing education requirements for certification and recertification.
Table 1 Subject Characteristics, by Depression (N=107) All Depressed Participants M (n=21) (GDS (SD) or N (%) Score [greater than or equal to] 5) Age# 63 (8.6) 62 (8.4) Months on hemodialysis# 86 (90) 62 (65.5) Sex# Male 55 (51%) 10 (48%) Female 52 (49%) 11 (52%) Race# White 35 (33%) 7 (20%) Black 69 (65%) 14 (20%) Other (a) 3 (2%) 0 (0%) Marital Status# Married 43 (40%) 9 (17%) Single 20 (19%) 0 (0%) Divorced 20 (19%) 4 (20%) Widowed 17 (16%) 4 (24%) Other (b) 7 (6%) 4 (57%) Health Status (c)# Poor or fair 49 (46%) 16 (33%) Good, very good, 58 (54%) 5 (9%) or excellent Disability Status (d)# 0.20 (0.30) 0.36 (0.43) Self-efficacy (e)# 67 (9.0) 61 (8.1) Not Depressed p' (n=86) (GDS Score < 5) Age# 64 (9.6) 0.4400 Months on hemodialysis# 68 (56.9) 0.8500 Sex# 0.7000 Male 45 (52%) Female 41 (48%) Race# 0.9400 White 28 (80%) Black 55 (80%) Other (a) 3 (100%) Marital Status# 0.0040 Married 34 (83%) Single 20 (100%) Divorced 16 (80%) Widowed 13 (76%) Other (b) 3 (43%) 0.0020 Health Status (c)# Poor or fair 33 (67%) Good, very good, 53 (91%) 0.0030 or excellent Disability Status (d)# 0.16 (0.25) 0.0006 Self-efficacy (e)# 69 (8.6) (a) Other races are American Indian and Asian. (b) Other marital statuses are not married but partnered, or separated. (c) Health status dichotomized as poor (i.e., poor, fair) and good (i.e., good, very good, excellent). (d) Disability status measured using HAQ scale (range 0 to 1.875). Higher scores indicate more difficulty with daily activities. (e) Self-efficacy possible range of scores 0 to 80. Higher scores indicate more self-efficacy. (f) Comparison of depressed and not depressed participants. Significance test using Chi-square or two-tailed t test. Items in bold indicate p-values <0.05. Note: Items in bold indicate p-values <0.05 are indicated with #. Table 2 Fluid Adherence and Nonadherence by Age Group (N=104) (a) Age Group Total in Age Group Adherent N (%) Nonadherent N (%) 50 to 59 43 16 (37%) 27 (63%) 60 to 69 39 21 (54%) 18 (46%) 70+ 22 18 (82%) 4 (18%) All ages 104 55 (53%) 49 (47%) (a) Chi-square (2)=11.65, p = 0.03; association analyzed using the Kruskall-Wallis test. Table 3 Logistic Regression Models of Fluid Adherence (N=104) (a) Adjusted Odds Adjusted Odds Ratio (95% Ratio (95% Confidence Confidence Interval) Interval) Depression (Not Depressed) Depressed 0.82 (0.67, 0.99)# 0.82 (0.68, 0.99)# Social support# 1.10 (0.93, 1.10) Self-efficacy# Age# 1.08 (1.02, 1.14)# 1.08 (1.07, 1.14)# Months on hemodialysis 0.99 (0.99, 1.00) 0.99 (0.99, 1.00) Sex (Female) Male 0.42 (0.17, 1.07)# 0.42 (0.17, 1.07) Psuedo R-square# 0.15 0.15 Goodness-of-fit (b)# p = 0.21 p = 0.19 Adjusted Odds Adjusted Odds Ratio (95% Ratio (95% Confidence Confidence Interval) Interval) Depression (Not Depressed) Depressed 0.89 (0.72, 1.09) 0.89 (0.72, 1.10) Social support# 1.00 (0.92, 1.09) Self-efficacy# 1.09 (1.03, 1.16)# 1.09 (1.03 1.16)# Age# 1.09 (1.02, 1.16)# 1.09 (1.02, 1.16)# Months on hemodialysis 0.99 (0.99, 1.00) 0.99 (0.99, 1.00) Sex (Female)# Male# 0.47 (0.18, 1.24) 0.48 (0.18, 1.25) Psuedo R-square# 0.21 0.21 Goodness-of-fit (b)# p = 0.29 p = 0.27 (a) Adjusted odds ratio and corresponding confidence intervals in bold indicate significance at the 0.05 level. (b) Goodness-of-fit assessed using the Hosmer-Lemeshow Test. P>0.05 indicates good fit. Note: (a) Adjusted odds ratio and corresponding confidence intervals in bold indicate significance at the 0.05 level are indicated with #.